Overview

Dataset statistics

Number of variables28
Number of observations15344
Missing cells15342
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 MiB
Average record size in memory224.0 B

Variable types

Categorical11
DateTime1
TimeSeries15
Numeric1

Alerts

State Code has constant value ""Constant
County Code has constant value ""Constant
Site Num has constant value ""Constant
Address has constant value ""Constant
State has constant value ""Constant
County has constant value ""Constant
City has constant value ""Constant
NO2 Units has constant value ""Constant
O3 Units has constant value ""Constant
SO2 Units has constant value ""Constant
CO Units has constant value ""Constant
NO2 Mean is highly overall correlated with NO2 1st Max Value and 7 other fieldsHigh correlation
NO2 1st Max Value is highly overall correlated with NO2 Mean and 4 other fieldsHigh correlation
NO2 AQI is highly overall correlated with NO2 Mean and 4 other fieldsHigh correlation
O3 Mean is highly overall correlated with NO2 Mean and 5 other fieldsHigh correlation
O3 1st Max Value is highly overall correlated with O3 Mean and 1 other fieldsHigh correlation
O3 AQI is highly overall correlated with O3 Mean and 1 other fieldsHigh correlation
SO2 Mean is highly overall correlated with SO2 1st Max Value and 2 other fieldsHigh correlation
SO2 1st Max Value is highly overall correlated with NO2 Mean and 5 other fieldsHigh correlation
SO2 AQI is highly overall correlated with NO2 Mean and 5 other fieldsHigh correlation
CO Mean is highly overall correlated with NO2 Mean and 8 other fieldsHigh correlation
CO 1st Max Value is highly overall correlated with NO2 Mean and 7 other fieldsHigh correlation
CO AQI is highly overall correlated with NO2 Mean and 7 other fieldsHigh correlation
SO2 AQI has 7672 (50.0%) missing valuesMissing
CO AQI has 7670 (50.0%) missing valuesMissing
NO2 Mean is non stationaryNon stationary
NO2 1st Max Value is non stationaryNon stationary
NO2 AQI is non stationaryNon stationary
O3 Mean is non stationaryNon stationary
O3 1st Max Value is non stationaryNon stationary
O3 AQI is non stationaryNon stationary
SO2 Mean is non stationaryNon stationary
SO2 1st Max Value is non stationaryNon stationary
SO2 1st Max Hour is non stationaryNon stationary
SO2 AQI is non stationaryNon stationary
CO Mean is non stationaryNon stationary
CO 1st Max Value is non stationaryNon stationary
CO AQI is non stationaryNon stationary
NO2 Mean is seasonalSeasonal
NO2 1st Max Value is seasonalSeasonal
NO2 AQI is seasonalSeasonal
O3 Mean is seasonalSeasonal
O3 1st Max Value is seasonalSeasonal
O3 AQI is seasonalSeasonal
SO2 Mean is seasonalSeasonal
SO2 1st Max Value is seasonalSeasonal
SO2 1st Max Hour is seasonalSeasonal
SO2 AQI is seasonalSeasonal
CO Mean is seasonalSeasonal
CO 1st Max Value is seasonalSeasonal
CO AQI is seasonalSeasonal
NO2 1st Max Hour has 1930 (12.6%) zerosZeros
SO2 Mean has 204 (1.3%) zerosZeros
SO2 1st Max Value has 204 (1.3%) zerosZeros
SO2 1st Max Hour has 2418 (15.8%) zerosZeros
SO2 AQI has 520 (3.4%) zerosZeros
CO 1st Max Hour has 2898 (18.9%) zerosZeros

Reproduction

Analysis started2023-03-17 09:45:09.579543
Analysis finished2023-03-17 09:46:09.302231
Duration59.72 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

State Code
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
4
15344 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15344
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 15344
100.0%

Length

2023-03-17T09:46:09.355865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T09:46:09.478499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
4 15344
100.0%

Most occurring characters

ValueCountFrequency (%)
4 15344
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15344
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15344
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 15344
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 15344
100.0%

County Code
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
13
15344 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters30688
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
13 15344
100.0%

Length

2023-03-17T09:46:09.576715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T09:46:09.699098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
13 15344
100.0%

Most occurring characters

ValueCountFrequency (%)
1 15344
50.0%
3 15344
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30688
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15344
50.0%
3 15344
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30688
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 15344
50.0%
3 15344
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 15344
50.0%
3 15344
50.0%

Site Num
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
9997
15344 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters61376
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9997
2nd row9997
3rd row9997
4th row9997
5th row9997

Common Values

ValueCountFrequency (%)
9997 15344
100.0%

Length

2023-03-17T09:46:09.798251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T09:46:09.915821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
9997 15344
100.0%

Most occurring characters

ValueCountFrequency (%)
9 46032
75.0%
7 15344
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61376
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 46032
75.0%
7 15344
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 46032
75.0%
7 15344
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 46032
75.0%
7 15344
 
25.0%

Address
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
4530 N 17TH AVENUE
15344 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters276192
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4530 N 17TH AVENUE
2nd row4530 N 17TH AVENUE
3rd row4530 N 17TH AVENUE
4th row4530 N 17TH AVENUE
5th row4530 N 17TH AVENUE

Common Values

ValueCountFrequency (%)
4530 N 17TH AVENUE 15344
100.0%

Length

2023-03-17T09:46:10.013204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T09:46:10.137054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
4530 15344
25.0%
n 15344
25.0%
17th 15344
25.0%
avenue 15344
25.0%

Most occurring characters

ValueCountFrequency (%)
46032
16.7%
N 30688
11.1%
E 30688
11.1%
4 15344
 
5.6%
5 15344
 
5.6%
3 15344
 
5.6%
0 15344
 
5.6%
1 15344
 
5.6%
7 15344
 
5.6%
T 15344
 
5.6%
Other values (4) 61376
22.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 138096
50.0%
Decimal Number 92064
33.3%
Space Separator 46032
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 30688
22.2%
E 30688
22.2%
T 15344
11.1%
H 15344
11.1%
A 15344
11.1%
V 15344
11.1%
U 15344
11.1%
Decimal Number
ValueCountFrequency (%)
4 15344
16.7%
5 15344
16.7%
3 15344
16.7%
0 15344
16.7%
1 15344
16.7%
7 15344
16.7%
Space Separator
ValueCountFrequency (%)
46032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 138096
50.0%
Latin 138096
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
46032
33.3%
4 15344
 
11.1%
5 15344
 
11.1%
3 15344
 
11.1%
0 15344
 
11.1%
1 15344
 
11.1%
7 15344
 
11.1%
Latin
ValueCountFrequency (%)
N 30688
22.2%
E 30688
22.2%
T 15344
11.1%
H 15344
11.1%
A 15344
11.1%
V 15344
11.1%
U 15344
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 276192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46032
16.7%
N 30688
11.1%
E 30688
11.1%
4 15344
 
5.6%
5 15344
 
5.6%
3 15344
 
5.6%
0 15344
 
5.6%
1 15344
 
5.6%
7 15344
 
5.6%
T 15344
 
5.6%
Other values (4) 61376
22.2%

State
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Arizona
15344 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters107408
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona 15344
100.0%

Length

2023-03-17T09:46:10.239660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T09:46:10.362174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
arizona 15344
100.0%

Most occurring characters

ValueCountFrequency (%)
A 15344
14.3%
r 15344
14.3%
i 15344
14.3%
z 15344
14.3%
o 15344
14.3%
n 15344
14.3%
a 15344
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 92064
85.7%
Uppercase Letter 15344
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 15344
16.7%
i 15344
16.7%
z 15344
16.7%
o 15344
16.7%
n 15344
16.7%
a 15344
16.7%
Uppercase Letter
ValueCountFrequency (%)
A 15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 107408
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15344
14.3%
r 15344
14.3%
i 15344
14.3%
z 15344
14.3%
o 15344
14.3%
n 15344
14.3%
a 15344
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15344
14.3%
r 15344
14.3%
i 15344
14.3%
z 15344
14.3%
o 15344
14.3%
n 15344
14.3%
a 15344
14.3%

County
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Maricopa
15344 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters122752
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaricopa
2nd rowMaricopa
3rd rowMaricopa
4th rowMaricopa
5th rowMaricopa

Common Values

ValueCountFrequency (%)
Maricopa 15344
100.0%

Length

2023-03-17T09:46:10.456907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T09:46:10.580976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
maricopa 15344
100.0%

Most occurring characters

ValueCountFrequency (%)
a 30688
25.0%
M 15344
12.5%
r 15344
12.5%
i 15344
12.5%
c 15344
12.5%
o 15344
12.5%
p 15344
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 107408
87.5%
Uppercase Letter 15344
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 30688
28.6%
r 15344
14.3%
i 15344
14.3%
c 15344
14.3%
o 15344
14.3%
p 15344
14.3%
Uppercase Letter
ValueCountFrequency (%)
M 15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 122752
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 30688
25.0%
M 15344
12.5%
r 15344
12.5%
i 15344
12.5%
c 15344
12.5%
o 15344
12.5%
p 15344
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 30688
25.0%
M 15344
12.5%
r 15344
12.5%
i 15344
12.5%
c 15344
12.5%
o 15344
12.5%
p 15344
12.5%

City
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Phoenix
15344 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters107408
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhoenix
2nd rowPhoenix
3rd rowPhoenix
4th rowPhoenix
5th rowPhoenix

Common Values

ValueCountFrequency (%)
Phoenix 15344
100.0%

Length

2023-03-17T09:46:10.680723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T09:46:10.805068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
phoenix 15344
100.0%

Most occurring characters

ValueCountFrequency (%)
P 15344
14.3%
h 15344
14.3%
o 15344
14.3%
e 15344
14.3%
n 15344
14.3%
i 15344
14.3%
x 15344
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 92064
85.7%
Uppercase Letter 15344
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 15344
16.7%
o 15344
16.7%
e 15344
16.7%
n 15344
16.7%
i 15344
16.7%
x 15344
16.7%
Uppercase Letter
ValueCountFrequency (%)
P 15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 107408
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 15344
14.3%
h 15344
14.3%
o 15344
14.3%
e 15344
14.3%
n 15344
14.3%
i 15344
14.3%
x 15344
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 15344
14.3%
h 15344
14.3%
o 15344
14.3%
e 15344
14.3%
n 15344
14.3%
i 15344
14.3%
x 15344
14.3%
Distinct3409
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Minimum2005-03-04 00:00:00
Maximum2016-03-26 00:00:00
2023-03-17T09:46:10.923880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:46:11.086690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Parts per billion
15344 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters260848
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion 15344
100.0%

Length

2023-03-17T09:46:11.237275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T09:46:11.363240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
parts 15344
33.3%
per 15344
33.3%
billion 15344
33.3%

Most occurring characters

ValueCountFrequency (%)
r 30688
11.8%
30688
11.8%
i 30688
11.8%
l 30688
11.8%
P 15344
 
5.9%
a 15344
 
5.9%
t 15344
 
5.9%
s 15344
 
5.9%
p 15344
 
5.9%
e 15344
 
5.9%
Other values (3) 46032
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 214816
82.4%
Space Separator 30688
 
11.8%
Uppercase Letter 15344
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 30688
14.3%
i 30688
14.3%
l 30688
14.3%
a 15344
7.1%
t 15344
7.1%
s 15344
7.1%
p 15344
7.1%
e 15344
7.1%
b 15344
7.1%
o 15344
7.1%
Space Separator
ValueCountFrequency (%)
30688
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 230160
88.2%
Common 30688
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 30688
13.3%
i 30688
13.3%
l 30688
13.3%
P 15344
6.7%
a 15344
6.7%
t 15344
6.7%
s 15344
6.7%
p 15344
6.7%
e 15344
6.7%
b 15344
6.7%
Other values (2) 30688
13.3%
Common
ValueCountFrequency (%)
30688
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 30688
11.8%
30688
11.8%
i 30688
11.8%
l 30688
11.8%
P 15344
 
5.9%
a 15344
 
5.9%
t 15344
 
5.9%
s 15344
 
5.9%
p 15344
 
5.9%
e 15344
 
5.9%
Other values (3) 46032
17.6%

NO2 Mean
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct1486
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.76471
Minimum0
Maximum55.208333
Zeros110
Zeros (%)0.7%
Memory size120.0 KiB
2023-03-17T09:46:11.466898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.8395831
Q111.742708
median18.375
Q325.25
95-th percentile32.874375
Maximum55.208333
Range55.208333
Interquartile range (IQR)13.507292

Descriptive statistics

Standard deviation8.6044527
Coefficient of variation (CV)0.4585444
Kurtosis-0.55529032
Mean18.76471
Median Absolute Deviation (MAD)6.76875
Skewness0.22912768
Sum287925.71
Variance74.036605
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.273069146 × 10-10
2023-03-17T09:46:11.621335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 110
 
0.7%
20.5 52
 
0.3%
24.583333 48
 
0.3%
17 48
 
0.3%
23.5 48
 
0.3%
15.458333 44
 
0.3%
17.916667 44
 
0.3%
9.25 44
 
0.3%
15.791667 44
 
0.3%
10.25 40
 
0.3%
Other values (1476) 14822
96.6%
ValueCountFrequency (%)
0 110
0.7%
2.166667 4
 
< 0.1%
2.333333 4
 
< 0.1%
2.75 4
 
< 0.1%
2.833333 8
 
0.1%
2.875 4
 
< 0.1%
2.958333 4
 
< 0.1%
3 8
 
0.1%
3.083333 4
 
< 0.1%
3.125 4
 
< 0.1%
ValueCountFrequency (%)
55.208333 4
< 0.1%
48.083333 4
< 0.1%
46.916667 4
< 0.1%
45.541667 4
< 0.1%
45.375 4
< 0.1%
45.181818 4
< 0.1%
44.666667 4
< 0.1%
44.458333 4
< 0.1%
43.375 4
< 0.1%
43.208333 4
< 0.1%
2023-03-17T09:46:11.838089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Value
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct349
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.199114
Minimum0
Maximum89
Zeros110
Zeros (%)0.7%
Memory size120.0 KiB
2023-03-17T09:46:12.105144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q130
median40
Q347
95-th percentile57
Maximum89
Range89
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.041413
Coefficient of variation (CV)0.34140618
Kurtosis-0.019160633
Mean38.199114
Median Absolute Deviation (MAD)8
Skewness-0.38113816
Sum586127.2
Variance170.07846
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.547506278 × 10-14
2023-03-17T09:46:12.256460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 508
 
3.3%
40 508
 
3.3%
44 504
 
3.3%
45 496
 
3.2%
42 476
 
3.1%
47 468
 
3.1%
43 460
 
3.0%
46 432
 
2.8%
38 424
 
2.8%
36 416
 
2.7%
Other values (339) 10652
69.4%
ValueCountFrequency (%)
0 110
0.7%
5 12
 
0.1%
6 20
 
0.1%
7 36
 
0.2%
8 20
 
0.1%
9 64
0.4%
10 56
0.4%
10.7 4
 
< 0.1%
10.8 4
 
< 0.1%
10.9 4
 
< 0.1%
ValueCountFrequency (%)
89 4
< 0.1%
76.5 4
< 0.1%
76 4
< 0.1%
75 4
< 0.1%
74.3 4
< 0.1%
73.9 4
< 0.1%
73.2 4
< 0.1%
73 4
< 0.1%
71.9 4
< 0.1%
71.4 4
< 0.1%
2023-03-17T09:46:12.466728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Hour
Numeric time series

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.479536
Minimum0
Maximum23
Zeros1930
Zeros (%)12.6%
Memory size120.0 KiB
2023-03-17T09:46:12.894879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median19
Q320
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9970729
Coefficient of variation (CV)0.55230174
Kurtosis-1.0306924
Mean14.479536
Median Absolute Deviation (MAD)2
Skewness-0.78642176
Sum222174
Variance63.953176
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.749391267 × 10-28
2023-03-17T09:46:13.037947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
20 2276
14.8%
19 2168
14.1%
21 2144
14.0%
0 1930
12.6%
18 1500
9.8%
22 1052
6.9%
6 828
 
5.4%
23 630
 
4.1%
7 576
 
3.8%
8 364
 
2.4%
Other values (14) 1876
12.2%
ValueCountFrequency (%)
0 1930
12.6%
1 268
 
1.7%
2 136
 
0.9%
3 104
 
0.7%
4 88
 
0.6%
5 344
 
2.2%
6 828
5.4%
7 576
 
3.8%
8 364
 
2.4%
9 336
 
2.2%
ValueCountFrequency (%)
23 630
 
4.1%
22 1052
6.9%
21 2144
14.0%
20 2276
14.8%
19 2168
14.1%
18 1500
9.8%
17 200
 
1.3%
16 32
 
0.2%
15 20
 
0.1%
14 20
 
0.1%
2023-03-17T09:46:13.222003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

NO2 AQI
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct70
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.030501
Minimum0
Maximum88
Zeros110
Zeros (%)0.7%
Memory size120.0 KiB
2023-03-17T09:46:13.488079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q128
median38
Q344
95-th percentile54
Maximum88
Range88
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.399921
Coefficient of variation (CV)0.34415068
Kurtosis0.072107141
Mean36.030501
Median Absolute Deviation (MAD)7
Skewness-0.3412747
Sum552852
Variance153.75805
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.252793809 × 10-14
2023-03-17T09:46:13.638426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 1124
 
7.3%
39 548
 
3.6%
40 544
 
3.5%
38 540
 
3.5%
44 540
 
3.5%
41 532
 
3.5%
25 524
 
3.4%
36 496
 
3.2%
45 480
 
3.1%
43 472
 
3.1%
Other values (60) 9544
62.2%
ValueCountFrequency (%)
0 110
0.7%
5 12
 
0.1%
6 20
 
0.1%
7 36
 
0.2%
8 84
0.5%
9 68
0.4%
10 152
1.0%
11 108
0.7%
12 92
0.6%
13 108
0.7%
ValueCountFrequency (%)
88 4
 
< 0.1%
74 8
 
0.1%
73 4
 
< 0.1%
72 4
 
< 0.1%
71 12
0.1%
69 16
0.1%
68 8
 
0.1%
67 12
0.1%
66 24
0.2%
65 24
0.2%
2023-03-17T09:46:13.834921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

O3 Units
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Parts per million
15344 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters260848
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million 15344
100.0%

Length

2023-03-17T09:46:14.093119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T09:46:14.217533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
parts 15344
33.3%
per 15344
33.3%
million 15344
33.3%

Most occurring characters

ValueCountFrequency (%)
r 30688
11.8%
30688
11.8%
i 30688
11.8%
l 30688
11.8%
P 15344
 
5.9%
a 15344
 
5.9%
t 15344
 
5.9%
s 15344
 
5.9%
p 15344
 
5.9%
e 15344
 
5.9%
Other values (3) 46032
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 214816
82.4%
Space Separator 30688
 
11.8%
Uppercase Letter 15344
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 30688
14.3%
i 30688
14.3%
l 30688
14.3%
a 15344
7.1%
t 15344
7.1%
s 15344
7.1%
p 15344
7.1%
e 15344
7.1%
m 15344
7.1%
o 15344
7.1%
Space Separator
ValueCountFrequency (%)
30688
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 230160
88.2%
Common 30688
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 30688
13.3%
i 30688
13.3%
l 30688
13.3%
P 15344
6.7%
a 15344
6.7%
t 15344
6.7%
s 15344
6.7%
p 15344
6.7%
e 15344
6.7%
m 15344
6.7%
Other values (2) 30688
13.3%
Common
ValueCountFrequency (%)
30688
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 30688
11.8%
30688
11.8%
i 30688
11.8%
l 30688
11.8%
P 15344
 
5.9%
a 15344
 
5.9%
t 15344
 
5.9%
s 15344
 
5.9%
p 15344
 
5.9%
e 15344
 
5.9%
Other values (3) 46032
17.6%

O3 Mean
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct1085
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.026245676
Minimum0.0005
Maximum0.06175
Zeros0
Zeros (%)0.0%
Memory size120.0 KiB
2023-03-17T09:46:14.317857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile0.0080063
Q10.016833
median0.026458
Q30.034833
95-th percentile0.0454107
Maximum0.06175
Range0.06125
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation0.011591475
Coefficient of variation (CV)0.44165274
Kurtosis-0.72229087
Mean0.026245676
Median Absolute Deviation (MAD)0.009042
Skewness0.10513022
Sum402.71365
Variance0.00013436228
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.158947717 × 10-6
2023-03-17T09:46:14.484303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.026917 64
 
0.4%
0.020042 60
 
0.4%
0.029875 52
 
0.3%
0.03425 48
 
0.3%
0.036083 48
 
0.3%
0.021875 44
 
0.3%
0.027542 40
 
0.3%
0.03175 40
 
0.3%
0.029958 40
 
0.3%
0.033333 40
 
0.3%
Other values (1075) 14868
96.9%
ValueCountFrequency (%)
0.0005 4
< 0.1%
0.000792 4
< 0.1%
0.000917 4
< 0.1%
0.001167 4
< 0.1%
0.001542 4
< 0.1%
0.001833 4
< 0.1%
0.002 4
< 0.1%
0.002125 8
0.1%
0.002458 4
< 0.1%
0.0025 4
< 0.1%
ValueCountFrequency (%)
0.06175 4
< 0.1%
0.05975 4
< 0.1%
0.058292 4
< 0.1%
0.057417 4
< 0.1%
0.057125 8
0.1%
0.056917 8
0.1%
0.055958 4
< 0.1%
0.055792 4
< 0.1%
0.05575 4
< 0.1%
0.055708 4
< 0.1%
2023-03-17T09:46:14.689398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Value
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct83
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.046224062
Minimum0.001
Maximum0.085
Zeros0
Zeros (%)0.0%
Memory size120.0 KiB
2023-03-17T09:46:14.951821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.02
Q10.036
median0.047
Q30.056
95-th percentile0.068
Maximum0.085
Range0.084
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.014541209
Coefficient of variation (CV)0.31458095
Kurtosis-0.27507683
Mean0.046224062
Median Absolute Deviation (MAD)0.01
Skewness-0.27980898
Sum709.262
Variance0.00021144676
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.863703613 × 10-6
2023-03-17T09:46:15.104531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.052 488
 
3.2%
0.055 488
 
3.2%
0.051 472
 
3.1%
0.046 468
 
3.1%
0.056 428
 
2.8%
0.053 414
 
2.7%
0.049 404
 
2.6%
0.054 404
 
2.6%
0.044 392
 
2.6%
0.047 388
 
2.5%
Other values (73) 10998
71.7%
ValueCountFrequency (%)
0.001 4
 
< 0.1%
0.002 16
0.1%
0.004 4
 
< 0.1%
0.005 4
 
< 0.1%
0.006 20
0.1%
0.007 16
0.1%
0.008 16
0.1%
0.009 32
0.2%
0.01 32
0.2%
0.011 28
0.2%
ValueCountFrequency (%)
0.085 8
 
0.1%
0.084 16
 
0.1%
0.083 8
 
0.1%
0.081 8
 
0.1%
0.08 8
 
0.1%
0.079 24
 
0.2%
0.078 16
 
0.1%
0.077 36
0.2%
0.076 64
0.4%
0.075 68
0.4%
2023-03-17T09:46:15.304467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Hour
Numeric time series

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.289234
Minimum0
Maximum23
Zeros76
Zeros (%)0.5%
Memory size120.0 KiB
2023-03-17T09:46:15.560565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q110
median10
Q311
95-th percentile12
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8789958
Coefficient of variation (CV)0.18261766
Kurtosis22.673546
Mean10.289234
Median Absolute Deviation (MAD)1
Skewness2.516659
Sum157878
Variance3.5306251
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.932374751 × 10-28
2023-03-17T09:46:15.686690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
10 7462
48.6%
11 3870
25.2%
9 2624
 
17.1%
12 468
 
3.1%
8 248
 
1.6%
13 128
 
0.8%
0 76
 
0.5%
23 68
 
0.4%
20 52
 
0.3%
14 48
 
0.3%
Other values (12) 300
 
2.0%
ValueCountFrequency (%)
0 76
 
0.5%
1 8
 
0.1%
3 4
 
< 0.1%
4 8
 
0.1%
6 28
 
0.2%
7 36
 
0.2%
8 248
 
1.6%
9 2624
 
17.1%
10 7462
48.6%
11 3870
25.2%
ValueCountFrequency (%)
23 68
0.4%
22 40
0.3%
21 44
0.3%
20 52
0.3%
19 32
0.2%
18 20
 
0.1%
17 28
0.2%
16 32
0.2%
15 20
 
0.1%
14 48
0.3%
2023-03-17T09:46:15.872093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

O3 AQI
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct84
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.418014
Minimum1
Maximum147
Zeros0
Zeros (%)0.0%
Memory size120.0 KiB
2023-03-17T09:46:16.142743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q131
median42
Q349
95-th percentile84
Maximum147
Range146
Interquartile range (IQR)18

Descriptive statistics

Standard deviation19.309748
Coefficient of variation (CV)0.44474048
Kurtosis2.3783968
Mean43.418014
Median Absolute Deviation (MAD)9
Skewness1.2410603
Sum666206
Variance372.86638
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.102256766 × 10-10
2023-03-17T09:46:16.324847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 872
 
5.7%
42 648
 
4.2%
36 604
 
3.9%
31 600
 
3.9%
44 570
 
3.7%
46 448
 
2.9%
43 436
 
2.8%
39 436
 
2.8%
45 390
 
2.5%
48 384
 
2.5%
Other values (74) 9956
64.9%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 16
 
0.1%
3 4
 
< 0.1%
4 4
 
< 0.1%
5 16
 
0.1%
6 20
 
0.1%
7 16
 
0.1%
8 64
0.4%
9 28
0.2%
10 12
 
0.1%
ValueCountFrequency (%)
147 4
 
< 0.1%
136 4
 
< 0.1%
133 4
 
< 0.1%
129 4
 
< 0.1%
126 8
 
0.1%
124 8
 
0.1%
122 28
0.2%
119 16
0.1%
115 4
 
< 0.1%
114 4
 
< 0.1%
2023-03-17T09:46:16.696796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

SO2 Units
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Parts per billion
15344 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters260848
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion 15344
100.0%

Length

2023-03-17T09:46:16.962943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T09:46:17.087956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
parts 15344
33.3%
per 15344
33.3%
billion 15344
33.3%

Most occurring characters

ValueCountFrequency (%)
r 30688
11.8%
30688
11.8%
i 30688
11.8%
l 30688
11.8%
P 15344
 
5.9%
a 15344
 
5.9%
t 15344
 
5.9%
s 15344
 
5.9%
p 15344
 
5.9%
e 15344
 
5.9%
Other values (3) 46032
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 214816
82.4%
Space Separator 30688
 
11.8%
Uppercase Letter 15344
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 30688
14.3%
i 30688
14.3%
l 30688
14.3%
a 15344
7.1%
t 15344
7.1%
s 15344
7.1%
p 15344
7.1%
e 15344
7.1%
b 15344
7.1%
o 15344
7.1%
Space Separator
ValueCountFrequency (%)
30688
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 230160
88.2%
Common 30688
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 30688
13.3%
i 30688
13.3%
l 30688
13.3%
P 15344
6.7%
a 15344
6.7%
t 15344
6.7%
s 15344
6.7%
p 15344
6.7%
e 15344
6.7%
b 15344
6.7%
Other values (2) 30688
13.3%
Common
ValueCountFrequency (%)
30688
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 30688
11.8%
30688
11.8%
i 30688
11.8%
l 30688
11.8%
P 15344
 
5.9%
a 15344
 
5.9%
t 15344
 
5.9%
s 15344
 
5.9%
p 15344
 
5.9%
e 15344
 
5.9%
Other values (3) 46032
17.6%

SO2 Mean
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct1061
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7240886
Minimum0
Maximum6.291667
Zeros204
Zeros (%)1.3%
Memory size120.0 KiB
2023-03-17T09:46:17.190187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.9344205
median1.6535715
Q32.333333
95-th percentile3.6119294
Maximum6.291667
Range6.291667
Interquartile range (IQR)1.3989125

Descriptive statistics

Standard deviation1.0387703
Coefficient of variation (CV)0.60250401
Kurtosis0.51592106
Mean1.7240886
Median Absolute Deviation (MAD)0.7089285
Skewness0.68882507
Sum26454.415
Variance1.0790437
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.903338346 × 10-5
2023-03-17T09:46:17.347440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 898
 
5.9%
3 268
 
1.7%
0 204
 
1.3%
1 134
 
0.9%
2.25 104
 
0.7%
2.041667 98
 
0.6%
1.708333 96
 
0.6%
1.75 90
 
0.6%
2.875 84
 
0.5%
2.375 82
 
0.5%
Other values (1051) 13286
86.6%
ValueCountFrequency (%)
0 204
1.3%
0.004348 2
 
< 0.1%
0.008333 2
 
< 0.1%
0.0125 12
 
0.1%
0.020833 4
 
< 0.1%
0.025 16
 
0.1%
0.029167 2
 
< 0.1%
0.0375 40
 
0.3%
0.041667 32
 
0.2%
0.05 14
 
0.1%
ValueCountFrequency (%)
6.291667 2
 
< 0.1%
6.2625 2
 
< 0.1%
5.958333 2
 
< 0.1%
5.925 2
 
< 0.1%
5.727273 2
 
< 0.1%
5.7 2
 
< 0.1%
5.666667 6
< 0.1%
5.65 4
 
< 0.1%
5.6375 2
 
< 0.1%
5.583333 10
0.1%
2023-03-17T09:46:17.553753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Value
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct81
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5849648
Minimum0
Maximum28
Zeros204
Zeros (%)1.3%
Memory size120.0 KiB
2023-03-17T09:46:17.819463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.7
Q11.6
median2
Q33
95-th percentile6
Maximum28
Range28
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.5544597
Coefficient of variation (CV)0.60134657
Kurtosis12.478214
Mean2.5849648
Median Absolute Deviation (MAD)1
Skewness1.8372027
Sum39663.7
Variance2.416345
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.92340525 × 10-8
2023-03-17T09:46:17.968495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3194
20.8%
3 2458
16.0%
4 928
 
6.0%
1 701
 
4.6%
5 520
 
3.4%
1.6 438
 
2.9%
1.3 388
 
2.5%
2.6 360
 
2.3%
2.3 354
 
2.3%
1.1 279
 
1.8%
Other values (71) 5724
37.3%
ValueCountFrequency (%)
0 204
1.3%
0.1 32
 
0.2%
0.2 60
 
0.4%
0.3 90
 
0.6%
0.4 94
 
0.6%
0.5 84
 
0.5%
0.6 186
1.2%
0.7 162
1.1%
0.8 251
1.6%
0.9 250
1.6%
ValueCountFrequency (%)
28 2
 
< 0.1%
19 2
 
< 0.1%
11 2
 
< 0.1%
10.6 2
 
< 0.1%
10 8
0.1%
9.6 2
 
< 0.1%
9.1 2
 
< 0.1%
9 18
0.1%
8.3 2
 
< 0.1%
8.1 2
 
< 0.1%
2023-03-17T09:46:18.163235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Hour
Numeric time series

NON STATIONARY  SEASONAL  ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.524635
Minimum0
Maximum23
Zeros2418
Zeros (%)15.8%
Memory size120.0 KiB
2023-03-17T09:46:18.423352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q317
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.0713379
Coefficient of variation (CV)0.84741703
Kurtosis-1.1384858
Mean9.524635
Median Absolute Deviation (MAD)6
Skewness0.48373705
Sum146146
Variance65.146496
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.605141039 × 10-20
2023-03-17T09:46:18.583728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 2427
15.8%
0 2418
15.8%
8 2119
13.8%
23 1928
12.6%
11 1133
7.4%
20 690
 
4.5%
7 628
 
4.1%
14 474
 
3.1%
21 454
 
3.0%
5 406
 
2.6%
Other values (14) 2667
17.4%
ValueCountFrequency (%)
0 2418
15.8%
1 249
 
1.6%
2 2427
15.8%
3 104
 
0.7%
4 74
 
0.5%
5 406
 
2.6%
6 346
 
2.3%
7 628
 
4.1%
8 2119
13.8%
9 366
 
2.4%
ValueCountFrequency (%)
23 1928
12.6%
22 374
 
2.4%
21 454
 
3.0%
20 690
 
4.5%
19 172
 
1.1%
18 84
 
0.5%
17 230
 
1.5%
16 32
 
0.2%
15 72
 
0.5%
14 474
 
3.1%
2023-03-17T09:46:18.772795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

SO2 AQI
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL  ZEROS 

Distinct14
Distinct (%)0.2%
Missing7672
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean3.6308655
Minimum0
Maximum40
Zeros520
Zeros (%)3.4%
Memory size120.0 KiB
2023-03-17T09:46:19.038808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile9
Maximum40
Range40
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5914284
Coefficient of variation (CV)0.71372196
Kurtosis12.450007
Mean3.6308655
Median Absolute Deviation (MAD)1
Skewness1.7793081
Sum27856
Variance6.7155014
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0001461336727
2023-03-17T09:46:19.166086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 2316
 
15.1%
4 1702
 
11.1%
1 1442
 
9.4%
6 790
 
5.1%
0 520
 
3.4%
7 386
 
2.5%
9 224
 
1.5%
10 176
 
1.1%
11 82
 
0.5%
13 20
 
0.1%
Other values (4) 14
 
0.1%
(Missing) 7672
50.0%
ValueCountFrequency (%)
0 520
 
3.4%
1 1442
9.4%
3 2316
15.1%
4 1702
11.1%
6 790
 
5.1%
7 386
 
2.5%
9 224
 
1.5%
10 176
 
1.1%
11 82
 
0.5%
13 20
 
0.1%
ValueCountFrequency (%)
40 2
 
< 0.1%
27 2
 
< 0.1%
16 2
 
< 0.1%
14 8
 
0.1%
13 20
 
0.1%
11 82
 
0.5%
10 176
 
1.1%
9 224
 
1.5%
7 386
2.5%
6 790
5.1%
2023-03-17T09:46:19.336737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

CO Units
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Parts per million
15344 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters260848
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million 15344
100.0%

Length

2023-03-17T09:46:19.602593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T09:46:19.729464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
parts 15344
33.3%
per 15344
33.3%
million 15344
33.3%

Most occurring characters

ValueCountFrequency (%)
r 30688
11.8%
30688
11.8%
i 30688
11.8%
l 30688
11.8%
P 15344
 
5.9%
a 15344
 
5.9%
t 15344
 
5.9%
s 15344
 
5.9%
p 15344
 
5.9%
e 15344
 
5.9%
Other values (3) 46032
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 214816
82.4%
Space Separator 30688
 
11.8%
Uppercase Letter 15344
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 30688
14.3%
i 30688
14.3%
l 30688
14.3%
a 15344
7.1%
t 15344
7.1%
s 15344
7.1%
p 15344
7.1%
e 15344
7.1%
m 15344
7.1%
o 15344
7.1%
Space Separator
ValueCountFrequency (%)
30688
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 230160
88.2%
Common 30688
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 30688
13.3%
i 30688
13.3%
l 30688
13.3%
P 15344
6.7%
a 15344
6.7%
t 15344
6.7%
s 15344
6.7%
p 15344
6.7%
e 15344
6.7%
m 15344
6.7%
Other values (2) 30688
13.3%
Common
ValueCountFrequency (%)
30688
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 30688
11.8%
30688
11.8%
i 30688
11.8%
l 30688
11.8%
P 15344
 
5.9%
a 15344
 
5.9%
t 15344
 
5.9%
s 15344
 
5.9%
p 15344
 
5.9%
e 15344
 
5.9%
Other values (3) 46032
17.6%

CO Mean
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct1973
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58345224
Minimum0.041667
Maximum2.6625
Zeros0
Zeros (%)0.0%
Memory size120.0 KiB
2023-03-17T09:46:19.835453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.041667
5-th percentile0.21001245
Q10.35068475
median0.495833
Q30.741667
95-th percentile1.216667
Maximum2.6625
Range2.620833
Interquartile range (IQR)0.39098225

Descriptive statistics

Standard deviation0.32657216
Coefficient of variation (CV)0.5597239
Kurtosis2.9378567
Mean0.58345224
Median Absolute Deviation (MAD)0.175
Skewness1.4428515
Sum8952.4912
Variance0.10664938
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.653971661 × 10-8
2023-03-17T09:46:19.993445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 158
 
1.0%
0.4 136
 
0.9%
0.429167 122
 
0.8%
0.395833 116
 
0.8%
0.379167 110
 
0.7%
0.516667 108
 
0.7%
0.391667 108
 
0.7%
0.45 108
 
0.7%
0.3375 106
 
0.7%
0.441667 106
 
0.7%
Other values (1963) 14166
92.3%
ValueCountFrequency (%)
0.041667 2
< 0.1%
0.058333 2
< 0.1%
0.0625 4
< 0.1%
0.068292 2
< 0.1%
0.070542 2
< 0.1%
0.070833 4
< 0.1%
0.074 2
< 0.1%
0.075 2
< 0.1%
0.077833 2
< 0.1%
0.079167 2
< 0.1%
ValueCountFrequency (%)
2.6625 2
< 0.1%
2.533333 2
< 0.1%
2.5 2
< 0.1%
2.408333 2
< 0.1%
2.395833 2
< 0.1%
2.334783 2
< 0.1%
2.333333 2
< 0.1%
2.322727 2
< 0.1%
2.283333 4
< 0.1%
2.270833 2
< 0.1%
2023-03-17T09:46:20.198242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Value
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct1008
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0562963
Minimum0.1
Maximum5.6
Zeros0
Zeros (%)0.0%
Memory size120.0 KiB
2023-03-17T09:46:20.618008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.6
median0.9
Q31.4
95-th percentile2.3
Maximum5.6
Range5.5
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.64825294
Coefficient of variation (CV)0.61370371
Kurtosis2.170699
Mean1.0562963
Median Absolute Deviation (MAD)0.4
Skewness1.2666385
Sum16207.81
Variance0.42023187
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.583848442 × 10-7
2023-03-17T09:46:20.772463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 1150
 
7.5%
0.5 1034
 
6.7%
0.4 1002
 
6.5%
0.7 936
 
6.1%
0.8 892
 
5.8%
0.9 744
 
4.8%
1.1 684
 
4.5%
1 680
 
4.4%
0.3 560
 
3.6%
1.2 552
 
3.6%
Other values (998) 7110
46.3%
ValueCountFrequency (%)
0.1 54
0.4%
0.116 2
 
< 0.1%
0.133 2
 
< 0.1%
0.138 2
 
< 0.1%
0.139 2
 
< 0.1%
0.147 4
 
< 0.1%
0.153 2
 
< 0.1%
0.156 4
 
< 0.1%
0.158 2
 
< 0.1%
0.166 2
 
< 0.1%
ValueCountFrequency (%)
5.6 2
< 0.1%
5.3 2
< 0.1%
4.7 2
< 0.1%
4.6 2
< 0.1%
4.5 2
< 0.1%
4.3 4
< 0.1%
4.2 4
< 0.1%
4.1 2
< 0.1%
4 4
< 0.1%
3.9 2
< 0.1%
2023-03-17T09:46:20.975885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Hour
Real number (ℝ)

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6493743
Minimum0
Maximum23
Zeros2898
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size120.0 KiB
2023-03-17T09:46:21.242500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation9.1020618
Coefficient of variation (CV)0.94328
Kurtosis-1.5193776
Mean9.6493743
Median Absolute Deviation (MAD)7
Skewness0.44344185
Sum148060
Variance82.847529
MonotonicityNot monotonic
2023-03-17T09:46:21.368973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 2898
18.9%
23 2000
13.0%
1 1466
9.6%
7 1226
8.0%
6 1140
 
7.4%
21 1110
 
7.2%
22 1094
 
7.1%
2 1052
 
6.9%
8 698
 
4.5%
20 642
 
4.2%
Other values (14) 2018
13.2%
ValueCountFrequency (%)
0 2898
18.9%
1 1466
9.6%
2 1052
 
6.9%
3 470
 
3.1%
4 190
 
1.2%
5 414
 
2.7%
6 1140
 
7.4%
7 1226
8.0%
8 698
 
4.5%
9 228
 
1.5%
ValueCountFrequency (%)
23 2000
13.0%
22 1094
7.1%
21 1110
7.2%
20 642
 
4.2%
19 278
 
1.8%
18 80
 
0.5%
17 22
 
0.1%
16 12
 
0.1%
15 36
 
0.2%
14 6
 
< 0.1%

CO AQI
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct37
Distinct (%)0.5%
Missing7670
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean10.370081
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Memory size120.0 KiB
2023-03-17T09:46:21.492507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median9
Q314
95-th percentile23
Maximum42
Range41
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.1681652
Coefficient of variation (CV)0.59480397
Kurtosis1.5807303
Mean10.370081
Median Absolute Deviation (MAD)4
Skewness1.1885429
Sum79580
Variance38.046262
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0008635620962
2023-03-17T09:46:21.629823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5 826
 
5.4%
7 816
 
5.3%
6 766
 
5.0%
8 622
 
4.1%
9 564
 
3.7%
3 494
 
3.2%
13 448
 
2.9%
10 414
 
2.7%
11 402
 
2.6%
14 342
 
2.2%
Other values (27) 1980
 
12.9%
(Missing) 7670
50.0%
ValueCountFrequency (%)
1 54
 
0.4%
2 194
 
1.3%
3 494
3.2%
5 826
5.4%
6 766
5.0%
7 816
5.3%
8 622
4.1%
9 564
3.7%
10 414
2.7%
11 402
2.6%
ValueCountFrequency (%)
42 2
 
< 0.1%
41 2
 
< 0.1%
40 2
 
< 0.1%
39 2
 
< 0.1%
38 4
 
< 0.1%
36 2
 
< 0.1%
35 10
0.1%
34 6
< 0.1%
33 10
0.1%
32 10
0.1%
2023-03-17T09:46:21.796736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Interactions

2023-03-17T09:46:06.107917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:34.509897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:36.530046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:38.592181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:40.786950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:42.893893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:45.030779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:47.268371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:49.318338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:51.516940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:53.623584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:55.802464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:57.755008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:59.863286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:46:02.082554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-03-17T09:45:42.516997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:44.645178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:46.718770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:48.944539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:51.113050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:53.243207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:55.431511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:57.402473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:59.483379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:46:01.708204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:46:03.737519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:46:05.743380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:46:08.024089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:36.287659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:38.344349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:40.541206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:42.647722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:44.776056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:46.851559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:49.069774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:51.252092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:53.372434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:55.554536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:57.521322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:59.616585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:46:01.835960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:46:03.861626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:46:05.863737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:46:08.147124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:36.411542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:38.470594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:40.660896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:42.769068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:44.899802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:46.976183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:49.194639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:51.386158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:53.498089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:55.678717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:57.635838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:45:59.737510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:46:01.959208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:46:03.980254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-17T09:46:05.985070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-17T09:46:22.072367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
NO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO MeanCO 1st Max ValueCO 1st Max HourCO AQI
NO2 Mean1.0000.8290.0650.829-0.734-0.407-0.147-0.4330.4380.5520.1300.5410.8310.8190.1360.831
NO2 1st Max Value0.8291.0000.2151.000-0.420-0.068-0.043-0.1000.4160.4840.1110.4800.6370.6860.2030.667
NO2 1st Max Hour0.0650.2151.0000.215-0.0010.1630.0760.157-0.012-0.0360.118-0.040-0.044-0.0400.305-0.110
NO2 AQI0.8291.0000.2151.000-0.421-0.070-0.044-0.1020.4150.4830.1120.4790.6370.6860.2030.667
O3 Mean-0.734-0.420-0.001-0.4211.0000.8550.3070.855-0.242-0.387-0.174-0.370-0.679-0.676-0.139-0.689
O3 1st Max Value-0.407-0.0680.163-0.0700.8551.0000.3310.991-0.118-0.231-0.108-0.220-0.441-0.430-0.020-0.463
O3 1st Max Hour-0.147-0.0430.076-0.0440.3070.3311.0000.327-0.066-0.110-0.096-0.107-0.197-0.200-0.047-0.209
O3 AQI-0.433-0.1000.157-0.1020.8550.9910.3271.000-0.180-0.280-0.082-0.277-0.464-0.450-0.019-0.483
SO2 Mean0.4380.416-0.0120.415-0.242-0.118-0.066-0.1801.0000.869-0.0870.8620.5270.4580.0450.470
SO2 1st Max Value0.5520.484-0.0360.483-0.387-0.231-0.110-0.2800.8691.0000.0920.9860.6080.5680.0720.580
SO2 1st Max Hour0.1300.1110.1180.112-0.174-0.108-0.096-0.082-0.0870.0921.0000.0660.0420.0680.2620.033
SO2 AQI0.5410.480-0.0400.479-0.370-0.220-0.107-0.2770.8620.9860.0661.0000.6080.5640.0620.577
CO Mean0.8310.637-0.0440.637-0.679-0.441-0.197-0.4640.5270.6080.0420.6081.0000.9010.0590.945
CO 1st Max Value0.8190.686-0.0400.686-0.676-0.430-0.200-0.4500.4580.5680.0680.5640.9011.0000.1541.000
CO 1st Max Hour0.1360.2030.3050.203-0.139-0.020-0.047-0.0190.0450.0720.2620.0620.0590.1541.0000.063
CO AQI0.8310.667-0.1100.667-0.689-0.463-0.209-0.4830.4700.5800.0330.5770.9451.0000.0631.000

Missing values

2023-03-17T09:46:08.390130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-17T09:46:08.905038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-17T09:46:09.210936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
041399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-04Parts per billion19.25000035.0033Parts per million0.0257500.0421136Parts per billion2.1250004.006.0Parts per million0.8458332.27NaN
141399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-04Parts per billion19.25000035.0033Parts per million0.0257500.0421136Parts per billion2.1250004.006.0Parts per million1.0166671.5217.0
241399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-04Parts per billion19.25000035.0033Parts per million0.0257500.0421136Parts per billion2.0875003.62NaNParts per million0.8458332.27NaN
341399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-04Parts per billion19.25000035.0033Parts per million0.0257500.0421136Parts per billion2.0875003.62NaNParts per million1.0166671.5217.0
441399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-05Parts per billion11.45833324.0723Parts per million0.0247080.0381032Parts per billion0.5416672.063.0Parts per million0.5166670.87NaN
541399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-05Parts per billion11.45833324.0723Parts per million0.0247080.0381032Parts per billion0.5416672.063.0Parts per million0.5083330.697.0
641399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-05Parts per billion11.45833324.0723Parts per million0.0247080.0381032Parts per billion0.5125001.68NaNParts per million0.5166670.87NaN
741399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-05Parts per billion11.45833324.0723Parts per million0.0247080.0381032Parts per billion0.5125001.68NaNParts per million0.5083330.697.0
841399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-06Parts per billion16.83333333.01931Parts per million0.0190830.0391033Parts per billion1.2083334.0216.0Parts per million0.6625001.120NaN
941399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-06Parts per billion16.83333333.01931Parts per million0.0190830.0391033Parts per billion1.2083334.0216.0Parts per million0.6000000.92310.0
State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
1533441399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-24Parts per billion20.19583341.02139Parts per million0.0226250.0491145Parts per billion0.2208331.191.0Parts per million0.5254171.03122NaN
1533541399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-24Parts per billion20.19583341.02139Parts per million0.0226250.0491145Parts per billion0.2208331.191.0Parts per million0.4583330.60057.0
1533641399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-25Parts per billion17.30833340.22238Parts per million0.0271250.0511147Parts per billion0.4250000.823NaNParts per million0.4535420.92023NaN
1533741399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-25Parts per billion17.30833340.22238Parts per million0.0271250.0511147Parts per billion0.4250000.823NaNParts per million0.4708330.80019.0
1533841399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-25Parts per billion17.30833340.22238Parts per million0.0271250.0511147Parts per billion0.4458331.001.0Parts per million0.4535420.92023NaN
1533941399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-25Parts per billion17.30833340.22238Parts per million0.0271250.0511147Parts per billion0.4458331.001.0Parts per million0.4708330.80019.0
1534041399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-26Parts per billion14.57916735.2033Parts per million0.0420530.0601167Parts per billion0.6375001.201.0Parts per million0.4541670.80039.0
1534141399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-26Parts per billion14.57916735.2033Parts per million0.0420530.0601167Parts per billion0.6000001.12NaNParts per million0.4330000.8921NaN
1534241399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-26Parts per billion14.57916735.2033Parts per million0.0420530.0601167Parts per billion0.6375001.201.0Parts per million0.4330000.8921NaN
1534341399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-26Parts per billion14.57916735.2033Parts per million0.0420530.0601167Parts per billion0.6000001.12NaNParts per million0.4541670.80039.0